




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer






class 		_lowerCAmelCase       (							__lowerCAmelCase	):
			"""simple docstring"""
			lowerCAmelCase__           ='''Wav2Vec2FeatureExtractor'''
			lowerCAmelCase__           ='''AutoTokenizer'''
			def __init__(					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE       )		->							int:





				"""simple docstring"""







				super().__init__(lowerCamelCase_     ,		lowerCamelCase_       )
				snake_case__						:     List[Any]		=self.feature_extractor
				snake_case__						:     Optional[Any]		=False
			@classmethod
			def 					UpperCAmelCase     (					cls     ,		__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							Any:





				"""simple docstring"""







				try:
					return super().from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_       )
				except OSError:
					warnings.warn(
					    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
					    ''' include a `tokenizer_class` attribute is deprecated and will be '''
					    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
					    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
					    '''file to suppress this warning: '''     ,		lowerCamelCase_     ,		)

					snake_case__						:     List[Any]		=WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_       )
					snake_case__						:     str		=WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_     ,		**lowerCamelCase_       )

					return cls(feature_extractor=lowerCamelCase_     ,		tokenizer=lowerCamelCase_       )
			def __call__(					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							Optional[int]:





				"""simple docstring"""







				if self._in_target_context_manager:
					return self.current_processor(*lowerCamelCase_     ,		**lowerCamelCase_       )

				if "raw_speech" in kwargs:
					warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
					snake_case__						:     int		=kwargs.pop('''raw_speech'''       )
				else:
					snake_case__						:     Any		=kwargs.pop('''audio'''     ,		lowerCamelCase_       )
				snake_case__						:     Optional[Any]		=kwargs.pop('''sampling_rate'''     ,		lowerCamelCase_       )
				snake_case__						:     Dict		=kwargs.pop('''text'''     ,		lowerCamelCase_       )
				if len(lowerCamelCase_       ) > 0:
					snake_case__						:     Tuple		=args[0]
					snake_case__						:     Tuple		=args[1:]

				if audio is None and text is None:
					raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

				if audio is not None:
					snake_case__						:     Optional[Any]		=self.feature_extractor(lowerCamelCase_     ,		*lowerCamelCase_     ,		sampling_rate=lowerCamelCase_     ,		**lowerCamelCase_       )
				if text is not None:
					snake_case__						:     Optional[Any]		=self.tokenizer(lowerCamelCase_     ,		**lowerCamelCase_       )

				if text is None:
					return inputs
				elif audio is None:
					return encodings
				else:
					snake_case__						:     Any		=encodings['''input_ids''']
					return inputs
			def 					UpperCAmelCase     (					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							Optional[Any]:





				"""simple docstring"""







				if self._in_target_context_manager:
					return self.current_processor.pad(*lowerCamelCase_     ,		**lowerCamelCase_       )

				snake_case__						:     Optional[int]		=kwargs.pop('''input_features'''     ,		lowerCamelCase_       )
				snake_case__						:     int		=kwargs.pop('''labels'''     ,		lowerCamelCase_       )
				if len(lowerCamelCase_       ) > 0:
					snake_case__						:     int		=args[0]
					snake_case__						:     Union[str, Any]		=args[1:]

				if input_features is not None:
					snake_case__						:     Any		=self.feature_extractor.pad(lowerCamelCase_     ,		*lowerCamelCase_     ,		**lowerCamelCase_       )
				if labels is not None:
					snake_case__						:     Optional[int]		=self.tokenizer.pad(lowerCamelCase_     ,		**lowerCamelCase_       )

				if labels is None:
					return input_features
				elif input_features is None:
					return labels
				else:
					snake_case__						:     int		=labels['''input_ids''']
					return input_features
			def 					UpperCAmelCase     (					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							List[Any]:





				"""simple docstring"""







				return self.tokenizer.batch_decode(*lowerCamelCase_     ,		**lowerCamelCase_       )
			def 					UpperCAmelCase     (					self     ,		*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       )		->							int:





				"""simple docstring"""







				return self.tokenizer.decode(*lowerCamelCase_     ,		**lowerCamelCase_       )


			@contextmanager
			def 					UpperCAmelCase     (					self       )		->							Tuple:





				"""simple docstring"""







				warnings.warn(
				    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
				    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
				    '''your audio inputs, or in a separate call.'''       )
				snake_case__						:     List[str]		=True
				snake_case__						:     List[str]		=self.tokenizer
				yield
				snake_case__						:     Dict		=self.feature_extractor
				snake_case__						:     Union[str, Any]		=False





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
